TypeError: Argument 'MLP( # attributes num_neurons_per_layer = [4, 1] )' of type <class '__main__.MLP'> is not a valid JAX type

I'm having some issues testing a basic model on Jax. For example, I'm trying to implement the value_and_grad() function from Jax manually for a binary classification problem. Here is my model initializer:

class MLP(nn.Module):
    num_neurons_per_layer: Sequence[int]

    @nn.compact
    def __call__(self, x):
        activation = x
        for i, num_neurons in enumerate(self.num_neurons_per_layer):
            activation = nn.Dense(num_neurons)(activation)
            if i != len(self.num_neurons_per_layer) - 1:
                activation = nn.relu(activation)
        return nn.sigmoid(activation)

And here is my BCE loss which is using vmap to batch the samples quicker all wrapped in a jit:

def make_bce_loss(xs, ys):
    
    def bce_loss(params, model): 
        def cross_entropy(x, y):
            preds = model.apply(params, x)
            return y * jnp.log(preds) + (1 - y) * jnp.log(1 - preds)
        return -jnp.mean(jax.vmap(cross_entropy)(xs, ys), axis=0)

    return jax.jit(bce_loss)

bce_loss = make_bce_loss(X, y)
value_and_grad_fn = jax.value_and_grad(bce_loss)

Then I proceed to create the model and init the parameters:

model = MLP(num_neurons_per_layer=[4, 1])
params = model.init(key, X)  # I create a jnp.array() to create X earlier on

When I test out my jitted version of value_and_grad_fn(params, model) I get the following error:

TypeError: Argument 'MLP( # attributes num_neurons_per_layer = [4, 1] )' of type <class '__main__.MLP'> is not a valid JAX type.

I'm not sure what I should be doing to correct this. It is throwing an error about the [4, 1] but those aren't involved in the calculation at all, they are only used to initialize the model in the MLP class.